Typically developing infants acquire language at a remarkable rate despite numerous perceptual and cognitive challenges. Infants may begin to learn language by tracking regularities in their environment. Specifically, research suggests that infants possess powerful computational mechanisms that may support the segmentation of words from fluent speech and facilitate word learning. The problem is that there is little research on the extent to which statistical regularities support early language acquisition under the challenging learning conditions often faced by young infants. The objective of the proposed research is to advance integrative and comprehensive theories of infant language acquisition by assessing how statistical learning supports (1) speech segmentation and word learning in background noise, (2) infants' ability to encode lexical representations in long-term memory, and (3) infants' abilities to represent newly segmented words with the appropriate level and type of detail to facilitate subsequent language learning. Three Aims will be addressed across nine experiments designed to test how statistical regularities found in natural language input support resilience, longevity, and representational specificity within a developmental framework. Infants will be familiarized with a short natural Italian language corpus and then tested on their ability o either discriminate words that have strong versus weak internal co-occurrence patterns (8- and 11-month-olds), or associate those words with novel objects (17-month-olds). Experiments are designed to tests how infants cope with simultaneous learning challenges. We will test the predictions that strong syllable co-occurrence patterns will bolster (1) speech segmentation and word learning in noise and (2) long-term memory for newly extracted words, and (3) that infants' word form representations will become more robust and specific. Results from the proposed project will advance our understanding of the learning mechanisms underlying normative language development. Individuals who are, for a variety of sensory, neurological, or developmental reasons, less adept at tracking and representing statistical regularities when faced with real-world learning challenges may be at greater risk for atypical language development. Results from the proposed research will be used to help generate and test hypotheses about the causal mechanisms for specific language delays in atypical populations, such as for infants with hearing loss or infants who, for various reasons, receive sub-optimal language input during critical developmental periods.